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Math

Coefficient of Variation Calculator (CV)

Enter a dataset and the tool applies CV = σ / μ × 100%, reporting both the sample (n − 1) and population (n) variants together with the mean, standard deviation and a quick interpretation against the conventional 10% / 30% thresholds. By normalising standard deviation against the mean, CV produces a unit-free, magnitude-free measure of relative dispersion — handy for comparing instrument repeatability, return volatility across stocks, or the consistency of two production batches.

Sample CV (uses n − 1)

42.76%

Standard choice for statistical inference: σ̂ / x̄ × 100%.

Sample standard deviation σ̂

2.1381

Population CV (uses n)

40.00%

Use this when the dataset is the entire population (e.g. all production batches).

Population standard deviation σ

2.0000

Summary statistics

Data points n
Sum Σ
Mean μ

Formula

CV = σ / μ (ratio) CV% = σ / μ × 100% (percent form) Sample: σ̂ = √( Σ(xᵢ − x̄)² / (n − 1) ) Population: σ = √( Σ(xᵢ − x̄)² / n ) Also known as the relative standard deviation (RSD); the two terms are interchangeable.

Frequently asked

How is the coefficient of variation different from the standard deviation?

Standard deviation σ is an absolute measure of dispersion in the original units of the data. The coefficient of variation CV = σ / μ is a relative measure, expressed as a pure ratio (or percentage), so you can compare it across units and magnitudes. Example: a batch of screws has diameter σ = 0.02 mm and a batch of I-beams has length σ = 0.02 m — the σ numbers look identical but cannot be compared directly. Their CVs, however (screws μ = 5 mm → 0.4%, I-beams μ = 6 m → 0.33%) tell you which process is more consistent.

Why is CV used to compare investment risk?

In portfolio theory, standard deviation captures absolute volatility, but expected returns differ a lot across instruments — a growth stock might have μ = 15% with σ = 25% while a value stock has μ = 6% with σ = 12%. Comparing σ directly makes the growth stock look "riskier". CV answers the question "how much volatility per unit of expected return?": growth CV = 1.67, value CV = 2.00 — by that yardstick the value stock has more volatility per unit of return. Professional risk-adjusted return measures like the Sharpe ratio (which also subtracts the risk-free rate) are more refined, but CV is the natural entry point.

My data has a mean of zero or a negative mean — can I still compute CV?

Mean = 0: CV is mathematically undefined (zero in the denominator). The tool reports "Undefined" rather than ∞. In practice if your data fluctuates around zero (e.g. daily stock returns) CV is the wrong tool — compare standard deviations directly, or use a different ratio whose denominator is meaningful. Negative mean: this tool divides by |μ| and reports CV as a positive percentage (the more common convention in the literature). But beware: if values cross zero and the mean is close to zero, CV becomes very unstable — switch to a robust dispersion measure such as the MAD, or work on a log-transformed scale.

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